1 / 30

A Scale and Rotation Invariant Approach to Tracking Human Body Part Regions in Videos

A Scale and Rotation Invariant Approach to Tracking Human Body Part Regions in Videos. Yihang Bo. Hao Jiang. Institute of Automation, CAS Boston College. Boston College. Challenges. Previous Rectangular Part Methods. Templates with Different scales . Templates with

maia
Download Presentation

A Scale and Rotation Invariant Approach to Tracking Human Body Part Regions in Videos

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A Scale and Rotation Invariant Approach to Tracking Human Body Part Regions in Videos Yihang Bo Hao Jiang Institute of Automation, CAS Boston College Boston College

  2. Challenges

  3. Previous Rectangular Part Methods Templates with Different scales Templates with Different rotations If the target scale and rotation are unknown, local part extraction becomes a very slow process.

  4. Solution: Finding Body Part Regions

  5. Overview of the Method • We track human body part regions (arm, leg and torso) in videos. • Our model considers spatial and temporal coupling among parts. • It is invariant to scale and rotation.

  6. Tracking Body Part Regions

  7. The Non-tree Model Body part coupling between two successive video frames

  8. Part Region Candidates Superpixels Object class independent Region Proposals P.F. Felzenszwalb and D.P. Huttenlocher, Efficient Graph-Based Image Segmentation International Journal of Computer Vision, Volume 59, Number 2, September 2004. Ian Endres, and Derek Hoiem, “Category Independent Object Proposals”, ECCV 2010.

  9. 3D Superpixels Video segmentation (3D superpixels) usually do not directly give human part regions.

  10. Partial Background Removal (Optional) warping warping warping warping … …

  11. Criteria Relative Ratio Shape Matching Part Distance Part Overlap Shape Changes Position Changes Appearance Changes

  12. Distance Term

  13. Overlap Region Overlap Region Overlap

  14. Size Ratio Part Size Ratio

  15. Shape Consistency Across Frames Shape Consistency

  16. Motion Smoothness Motion Continuity

  17. Color Consistency Appearance Consistency

  18. Inference on a Loopy Graph … We assign region candidates to each of the body part node so that the objective function is minimized.

  19. Convert to a Chain … … Linear meta-graph

  20. Convert to a Chain … … Unfortunately, there are too many whole body configurations in each video frame.

  21. Convert to a Chain … … Solution: we find the best-N whole body configurations in each video frame.

  22. Cycle Removal

  23. Cycle Breaking

  24. Find Best-N Body Configurations on a Cycle Best-N (with torso1) + Best-N (with torso2) Best-N (with torso1,2) + Best-N (with torso3) Best-N (with torso1,2,3) … + Best-N (with torso M) Best-N (with torso1..M)

  25. Region Tracking on a Trellis Best-N Body configurations Frame 1 Frame 2 Frame k

  26. Sample Results on Five Test Videos V1 V2 V3 V4 V5

  27. Comparison Result [N-best] D. Park, D. Ramanan. "N-Best Maximal Decoders for Part Models”, ICCV 2011.

  28. Comparison Result Quantitative results

  29. Conclusion • By tracking body part regions, we can achieve efficient scale and rotation invariant human pose tracking. • This method can be used for human tracking in complex sports videos.

  30. Thank You

More Related